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AI Agent Inventory for Compliance
Plan an AI agent inventory for compliance reviews: define scope, assign accountable owners, preserve evidence and keep decisions in context.
AI agent inventory tool for manual records
This AI agent inventory tool turns a JSON array or local JSON file you supply into reviewable records. Use it as an AI inventory tool after your team assembles its source list; it does not discover agents or scan accounts. The generated CSV can serve as an AI agent inventory CSV template for your own records, but this page does not provide a separate blank workbook. Paste a JSON array or read a local JSON file. Every field must be a string. Use an empty string for missing owner, purpose, platform, tools, permissions or review date. Use unknown for data class, autonomy or environment. Store references to credentials, never credential values.
Set the boundary before collecting records
An AI agent inventory for compliance begins with a clear question: which deployed workflows and agent uses must your team be able to explain? Define business units, environments and sources before counting records. Treat a deployment or materially different workflow as the unit when its purpose, data, connected tools or decision authority differs. One model may support unrelated uses, so a model name alone is not a reliable boundary.
Agree who can confirm each source and what evidence supports a record. Procurement approvals, service catalogs, deployment manifests, identity logs and owner interviews answer different questions; none alone proves the inventory is complete. Record source, collection date, responsible owner and unresolved gaps so a later reviewer can tell what was checked.
Connect records to accountable decisions
For an AI agent inventory for audit preparation, distinguish an observed fact from an owner statement, estimate or open question. Link the record to business purpose, people affected, data categories, permissions and human checkpoints. A list can help an internal audit team trace who reviewed a use and what evidence they considered, but it does not certify a control, establish legal compliance or replace the organization’s audit process.
Use the same boundary in change review. When a team adds a connector, changes a permission or moves a workflow into production, ask whether intended use, data, owner or approval path changed. Keep the decision and evidence in the system of record your organization authorizes. Do not store secret values; keep an approved credential reference instead.
Make reviews repeatable
Assign a business owner and technical contact, set a review cadence that follows policy, and state what event triggers an earlier check. Ask the owner to confirm purpose and permitted actions against current configuration. Route unknowns to a named person. A high-priority review label can organize attention, but is not a legal classification or a conclusion about model safety.
Begin with one representative workflow, agree the fields and evidence rules, then extend the method to other teams. Keep a dated export when the inventory changes so the organization can explain what was known at a decision point. The goal is a useful governance record, not a claim that every agent has been found.
Choose the next step
Use the AI agent inventory builder to enter records manually and export a local CSV. Start with the AI agent inventory spreadsheet to agree on fields, or use the shadow AI discovery guide to plan authorized source checks. Teams documenting model components can compare the AI bill of materials guide; teams tracking MCP deployments can use the MCP server inventory guide.
Sources and limits
NIST’s AI Risk Management Framework is voluntary and describes a Map function for documenting context and intended use. ISO/IEC 42001 is an AI management-system standard. The EU AI Act contains context-dependent classification and registration provisions; an inventory is not itself an assessment under the Act. Use linked primary sources and qualified counsel for a specific system.
Updated 2026-10-08. Sources are linked on this page.
Primary sources and review
- NIST AI Risk Management Framework: voluntary risk-management context
- ISO/IEC 42001:2023: AI management system overview
- Regulation (EU) 2024/1689 (Artificial Intelligence Act): official EUR-Lex text, including Articles 6, 49 and 71
- Model Context Protocol: official specification
- OWASP LLM06:2025: permissions, functionality and autonomy
Published by Agent Inventory Tool. Updated . Sources are linked on this page. Outputs do not certify compliance.